Carseats R Notebook
Introduction
Here is my analysis of data/Carseats_org.csv.
Configuration
library(leaps)
library(data.table)
library(stringr)
library(caret)
library(ggplot2)
library(DataExplorer)
library(dplyr)
library(ggExtra)
library(RColorBrewer)
library(plotly)
library(corrplot)
library(htmltools)
library(MASS)
library(GPArotation)
library(psych)
library(mgcv)
library(tidyverse)
library(mltools)
library(e1071)System Information
Due to the large number of libraries in use I have provided system information.
sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS High Sierra 10.13.6
##
## Matrix products: default
## BLAS/LAPACK: /anaconda3/lib/R/lib/libRblas.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] e1071_1.7-0.1 mltools_0.3.5 forcats_0.3.0
## [4] purrr_0.2.5 readr_1.3.1 tidyr_0.8.2
## [7] tibble_2.0.1 tidyverse_1.2.1 mgcv_1.8-26
## [10] nlme_3.1-137 psych_1.8.12 GPArotation_2014.11-1
## [13] MASS_7.3-51.1 htmltools_0.3.6 corrplot_0.84
## [16] plotly_4.8.0 RColorBrewer_1.1-2 ggExtra_0.8
## [19] dplyr_0.8.0.1 DataExplorer_0.7.0 caret_6.0-81
## [22] ggplot2_3.1.0 lattice_0.20-38 stringr_1.4.0
## [25] data.table_1.12.0 leaps_3.0
##
## loaded via a namespace (and not attached):
## [1] lubridate_1.7.4 httr_1.4.0 tools_3.5.1
## [4] backports_1.1.3 R6_2.4.0 rpart_4.1-13
## [7] lazyeval_0.2.1 colorspace_1.4-0 nnet_7.3-12
## [10] withr_2.1.2 tidyselect_0.2.5 gridExtra_2.3
## [13] mnormt_1.5-5 compiler_3.5.1 cli_1.0.1
## [16] rvest_0.3.2 xml2_1.2.0 scales_1.0.0
## [19] digest_0.6.18 foreign_0.8-71 rmarkdown_1.11
## [22] pkgconfig_2.0.2 htmlwidgets_1.3 rlang_0.3.1
## [25] readxl_1.3.0 rstudioapi_0.9.0 shiny_1.2.0
## [28] generics_0.0.2 jsonlite_1.6 ModelMetrics_1.1.0
## [31] magrittr_1.5 Matrix_1.2-15 Rcpp_1.0.0
## [34] munsell_0.5.0 stringi_1.3.1 yaml_2.2.0
## [37] plyr_1.8.4 recipes_0.1.4 grid_3.5.1
## [40] parallel_3.5.1 promises_1.0.1 crayon_1.3.4
## [43] miniUI_0.1.1.1 haven_2.0.0 splines_3.5.1
## [46] hms_0.4.2 knitr_1.21 pillar_1.3.1
## [49] igraph_1.2.4 reshape2_1.4.3 codetools_0.2-16
## [52] stats4_3.5.1 glue_1.3.0 evaluate_0.12
## [55] modelr_0.1.3 httpuv_1.4.5.1 foreach_1.4.4
## [58] networkD3_0.4 cellranger_1.1.0 gtable_0.2.0
## [61] assertthat_0.2.0 xfun_0.4 gower_0.1.2
## [64] mime_0.6 prodlim_2018.04.18 xtable_1.8-3
## [67] broom_0.5.1 later_0.8.0 class_7.3-15
## [70] survival_2.43-3 viridisLite_0.3.0 timeDate_3043.102
## [73] iterators_1.0.10 lava_1.6.5 ipred_0.9-8sapply(c('repr', 'IRdisplay', 'IRkernel'), function(p) paste(packageVersion(p)))## repr IRdisplay IRkernel
## "0.19.2" "0.7.0" "0.8.15"Data
I load the data into r, and drop the “ID” column.
carseats <- read.csv("data/Carseats_org.csv", header = T, stringsAsFactors = T)
drops <- c("X")
carseats <- carseats[ , !(names(carseats) %in% drops)]
head(carseats, 10)## Sales CompPrice Income Advertising Population Price ShelveLoc Age
## 1 9.50 138 73 11 276 120 Bad 42
## 2 11.22 111 48 16 260 83 Good 65
## 3 10.06 113 35 10 269 80 Medium 59
## 4 7.40 117 100 4 466 97 Medium 55
## 5 4.15 141 64 3 340 128 Bad 38
## 6 10.81 124 113 13 501 72 Bad 78
## 7 6.63 115 105 0 45 108 Medium 71
## 8 11.85 136 81 15 425 120 Good 67
## 9 6.54 132 110 0 108 124 Medium 76
## 10 4.69 132 113 0 131 124 Medium 76
## Education Urban US
## 1 17 Yes Yes
## 2 10 Yes Yes
## 3 12 Yes Yes
## 4 14 Yes Yes
## 5 13 Yes No
## 6 16 No Yes
## 7 15 Yes No
## 8 10 Yes Yes
## 9 10 No No
## 10 17 No YesHere I create two new data frames to manage numeric and categorical data.
# get vectors of continuous and categorical cols
nums <- dplyr::select_if(carseats, is.numeric)
cats <- dplyr::select_if(carseats, is.factor)
nums[sample(nrow(nums), 10), ]## Sales CompPrice Income Advertising Population Price Age Education
## 191 8.79 130 37 13 297 101 37 13
## 175 0.00 139 24 0 358 185 79 15
## 176 7.54 115 89 0 38 122 25 12
## 345 8.43 138 80 0 108 126 70 13
## 218 4.34 106 44 0 481 111 70 14
## 311 9.53 175 65 29 419 166 53 12
## 55 4.90 134 103 13 25 144 76 17
## 393 4.53 129 42 13 315 130 34 13
## 357 3.58 142 109 0 111 164 72 12
## 279 7.22 114 113 2 129 151 40 15cats[sample(nrow(cats), 10), ]## ShelveLoc Urban US
## 353 Good Yes Yes
## 77 Medium Yes Yes
## 287 Medium No Yes
## 25 Bad Yes Yes
## 123 Medium Yes Yes
## 216 Bad Yes Yes
## 42 Bad Yes No
## 180 Medium Yes Yes
## 104 Bad Yes Yes
## 397 Medium No YesNumeric Summaries
summary(nums)## Sales CompPrice Income Advertising
## Min. : 0.000 Min. : 77 Min. : 21.00 Min. : 0.000
## 1st Qu.: 5.390 1st Qu.:115 1st Qu.: 42.75 1st Qu.: 0.000
## Median : 7.490 Median :125 Median : 69.00 Median : 5.000
## Mean : 7.496 Mean :125 Mean : 68.66 Mean : 6.635
## 3rd Qu.: 9.320 3rd Qu.:135 3rd Qu.: 91.00 3rd Qu.:12.000
## Max. :16.270 Max. :175 Max. :120.00 Max. :29.000
## Population Price Age Education
## Min. : 10.0 Min. : 24.0 Min. :25.00 Min. :10.0
## 1st Qu.:139.0 1st Qu.:100.0 1st Qu.:39.75 1st Qu.:12.0
## Median :272.0 Median :117.0 Median :54.50 Median :14.0
## Mean :264.8 Mean :115.8 Mean :53.32 Mean :13.9
## 3rd Qu.:398.5 3rd Qu.:131.0 3rd Qu.:66.00 3rd Qu.:16.0
## Max. :509.0 Max. :191.0 Max. :80.00 Max. :18.0str(nums)## 'data.frame': 400 obs. of 8 variables:
## $ Sales : num 9.5 11.22 10.06 7.4 4.15 ...
## $ CompPrice : int 138 111 113 117 141 124 115 136 132 132 ...
## $ Income : int 73 48 35 100 64 113 105 81 110 113 ...
## $ Advertising: int 11 16 10 4 3 13 0 15 0 0 ...
## $ Population : int 276 260 269 466 340 501 45 425 108 131 ...
## $ Price : int 120 83 80 97 128 72 108 120 124 124 ...
## $ Age : int 42 65 59 55 38 78 71 67 76 76 ...
## $ Education : int 17 10 12 14 13 16 15 10 10 17 ...Categorical Summaries
summary(cats)## ShelveLoc Urban US
## Bad : 96 No :118 No :142
## Good : 85 Yes:282 Yes:258
## Medium:219str(cats)## 'data.frame': 400 obs. of 3 variables:
## $ ShelveLoc: Factor w/ 3 levels "Bad","Good","Medium": 1 2 3 3 1 1 3 2 3 3 ...
## $ Urban : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 2 2 1 1 ...
## $ US : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 2 1 2 1 2 ...Data Dimensionality
This command is to inspect the different data types in the data.
str(carseats)## 'data.frame': 400 obs. of 11 variables:
## $ Sales : num 9.5 11.22 10.06 7.4 4.15 ...
## $ CompPrice : int 138 111 113 117 141 124 115 136 132 132 ...
## $ Income : int 73 48 35 100 64 113 105 81 110 113 ...
## $ Advertising: int 11 16 10 4 3 13 0 15 0 0 ...
## $ Population : int 276 260 269 466 340 501 45 425 108 131 ...
## $ Price : int 120 83 80 97 128 72 108 120 124 124 ...
## $ ShelveLoc : Factor w/ 3 levels "Bad","Good","Medium": 1 2 3 3 1 1 3 2 3 3 ...
## $ Age : int 42 65 59 55 38 78 71 67 76 76 ...
## $ Education : int 17 10 12 14 13 16 15 10 10 17 ...
## $ Urban : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 2 2 1 1 ...
## $ US : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 2 1 2 1 2 ...print(paste('Number of Columns:', ncol(carseats)))## [1] "Number of Columns: 11"print(paste('Number of Numeric Columns:', ncol(nums)))## [1] "Number of Numeric Columns: 8"print(paste('Number of Categorical Columns:', ncol(cats)))## [1] "Number of Categorical Columns: 3"dim(carseats)## [1] 400 11dim(nums)## [1] 400 8dim(cats)## [1] 400 3Here’s a quick way to examine general properties of the data:
DataExplorer::introduce(data=carseats)## rows columns discrete_columns continuous_columns all_missing_columns
## 1 400 11 3 8 0
## total_missing_values complete_rows total_observations memory_usage
## 1 0 400 4400 24000Finally, I want to look at the first and last rows of the data set. Just to be safe:
head(carseats, 2)## Sales CompPrice Income Advertising Population Price ShelveLoc Age
## 1 9.50 138 73 11 276 120 Bad 42
## 2 11.22 111 48 16 260 83 Good 65
## Education Urban US
## 1 17 Yes Yes
## 2 10 Yes Yestail(carseats, 2)## Sales CompPrice Income Advertising Population Price ShelveLoc Age
## 399 5.94 100 79 7 284 95 Bad 50
## 400 9.71 134 37 0 27 120 Good 49
## Education Urban US
## 399 12 Yes Yes
## 400 16 Yes Yeshead(nums, 2)## Sales CompPrice Income Advertising Population Price Age Education
## 1 9.50 138 73 11 276 120 42 17
## 2 11.22 111 48 16 260 83 65 10tail(nums, 2)## Sales CompPrice Income Advertising Population Price Age Education
## 399 5.94 100 79 7 284 95 50 12
## 400 9.71 134 37 0 27 120 49 16head(cats, 2)## ShelveLoc Urban US
## 1 Bad Yes Yes
## 2 Good Yes Yestail(cats, 2)## ShelveLoc Urban US
## 399 Bad Yes Yes
## 400 Good Yes YesNumeric Plotting
I start out with a few general scatter plots.
plot_ly(data=carseats,
x=~Age,
y=~Sales,
mode = 'markers',
type = 'scatter',
color=~ShelveLoc) %>%
layout(title = "Age, Shelf Location, and Sales Scatter Plot", width=900)## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()This plot below shows good separation and a weak linear trend. These variables are worth investigating.
plot_ly(data=carseats,
x=~Price,
y=~Sales,
mode = 'markers',
type = 'scatter',
color=~ShelveLoc) %>%
layout(title = "Price, Shelf Location, and Sales Scatter Plot", width=900)## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()Here we inspect the density of the ‘Price vs Sales’ relationship:
plot_ly(data=carseats,
x=~Price,
y=~Sales,
mode = 'markers',
size = ~Price,
type = 'scatter',
colors = "Dark2",
alpha = .6) %>%
layout(title = "Price, US, and Sales Scatter Plot", width=900)## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()## Warning: `line.width` does not currently support multiple values.Normalization
I choose to normalize the numeric data in order to be able to plot each variable on the same scale. This will allow me to investigate the variation of each predictor relative to Sales.
preObj <- preProcess(nums, method=c("center", "scale"))
scaled.nums <- predict(preObj, nums)
head(scaled.nums, 2)## Sales CompPrice Income Advertising Population Price
## 1 0.7094877 0.8493913 0.1551667 0.656355 0.07572445 0.177601
## 2 1.3185281 -0.9113430 -0.7381360 1.408194 -0.03284107 -1.385119
## Age Education
## 1 -0.6989069 1.182968
## 2 0.7208201 -1.488250tail(scaled.nums, 2)## Sales CompPrice Income Advertising Population Price
## 399 -0.5510841 -1.6286792 0.3695593 0.05488421 0.1300072 -0.8782909
## 400 0.7838473 0.5885417 -1.1311891 -0.99768973 -1.6138265 0.1776010
## Age Education
## 399 -0.2050888 -0.7250447
## 400 -0.2668161 0.8013652str(scaled.nums)## 'data.frame': 400 obs. of 8 variables:
## $ Sales : num 0.7095 1.3185 0.9078 -0.0341 -1.1849 ...
## $ CompPrice : num 0.849 -0.911 -0.781 -0.52 1.045 ...
## $ Income : num 0.155 -0.738 -1.203 1.12 -0.166 ...
## $ Advertising: num 0.656 1.408 0.506 -0.396 -0.547 ...
## $ Population : num 0.0757 -0.0328 0.0282 1.3649 0.51 ...
## $ Price : num 0.178 -1.385 -1.512 -0.794 0.515 ...
## $ Age : num -0.699 0.721 0.35 0.104 -0.946 ...
## $ Education : num 1.183 -1.4882 -0.725 0.0382 -0.3434 ...print("")## [1] ""summary(scaled.nums)## Sales CompPrice Income
## Min. :-2.65440 Min. :-3.12856 Min. :-1.70290
## 1st Qu.:-0.74584 1st Qu.:-0.65049 1st Qu.:-0.92573
## Median :-0.00224 Median : 0.00163 Median : 0.01224
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.64575 3rd Qu.: 0.65375 3rd Qu.: 0.79834
## Max. : 3.10670 Max. : 3.26225 Max. : 1.83458
## Advertising Population Price
## Min. :-0.9977 Min. :-1.72918 Min. :-3.87702
## 1st Qu.:-0.9977 1st Qu.:-0.85387 1st Qu.:-0.66711
## Median :-0.2459 Median : 0.04858 Median : 0.05089
## Mean : 0.0000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.8067 3rd Qu.: 0.90693 3rd Qu.: 0.64219
## Max. : 3.3630 Max. : 1.65671 Max. : 3.17633
## Age Education
## Min. :-1.74827 Min. :-1.48825
## 1st Qu.:-0.83779 1st Qu.:-0.72504
## Median : 0.07268 Median : 0.03816
## Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.78255 3rd Qu.: 0.80137
## Max. : 1.64673 Max. : 1.56457Distributions
Here are scaled distributions (histograms and density plots) for each numeric variable, including Sales. The variables relating to money ($) tend to be approximately normal. Many other variables tend to be approximately uniform, which does not bode well for their predictive power.
scaled.nums %>%
tidyr::gather() %>%
ggplot(aes(x=value,y=..density..))+
ggtitle('Distributions of Continous Variables (scaled)') +
facet_wrap(~ key, scales = "free") +
geom_histogram(fill=I("orange"), col=I("black"), bins = 50) +
facet_wrap(~ key, scales = "free") +
geom_density(color="blue", fill='light blue', alpha = 0.4)
Here we plot all numeric variables against their distributions. This is just another way to examine the information shown above.
scaled.nums %>%
tidyr::gather() %>%
plot_ly(x=~key, y=~value,
type = "box",
boxpoints = "all",
jitter = 0.4,
pointpos = 0,
color = ~key,
colors = "Dark2") %>%
subplot(shareX = TRUE) %>%
layout(title = "Numeric Variable Distributions (scaled)",
yaxis=list(title='Standard Deviation'),
xaxis=list(title='Variable'),
autosize=FALSE,
width=900,
height=500)## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()Scatterplots
Here we plot all numeric variables against Sales (scaled). This allows us to investigate possible linear relationships between that variable and Sales. As shown below, only ‘Price’ appears to have a linear relationship worth investigating. This took me so long to figure out.
numeric.scatterplots <- htmltools::tagList()
count = 1
for (i in names(scaled.nums[,-1])) {
numeric.scatterplots[[count]] <- plot_ly(scaled.nums, x=scaled.nums[,i], y=scaled.nums$Sales,
colors = 'RdYlGn',
mode = 'markers',
type = 'scatter',
size = scaled.nums$Sales^2,
color = scaled.nums$Sales,
marker = list(line = list(color = 'black',width = 2)),
name=paste(i)) %>%
layout(title = paste(i, "vs Sales (scaled)<br>Size=Sales^2"),
yaxis=list(title='Sales'),
xaxis=list(title=i),
showlegend = FALSE)
count = count + 1
}
numeric.scatterplots## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.By adding naive regression lines to a few scatter plots we can confirm our suspicions:
fit.Pop <- lm(Sales ~ Population, data = scaled.nums)
fit.Age <- lm(Sales ~ Age, data = scaled.nums)
fit.CompPrice <- lm(Sales ~ CompPrice, data = scaled.nums)
fit.Price <- lm(Sales ~ Price, data = scaled.nums)
regression.scatterplots <- htmltools::tagList()
regression.scatterplots[[1]] <- plot_ly(scaled.nums,
x = ~Population,
name = 'Population vs Sales Regression Line') %>%
add_markers(y = ~Sales,
name = 'Population vs Sales Observations') %>%
add_lines(x = ~Population,
y = fitted(fit.Pop)) %>%
layout(title = "Population vs Sales",
yaxis=list(title='Sales',
xaxis=list(title='Population')),
showlegend = FALSE)
regression.scatterplots[[2]] <- plot_ly(scaled.nums,
x = ~Age,
name = 'Age vs Sales Regression Line') %>%
add_markers(y = ~Sales,
name = 'Age vs Sales Observations') %>%
add_lines(x = ~Age,
y = fitted(fit.Age)) %>%
layout(title = "Age vs Sales",
yaxis=list(title='Sales',
xaxis=list(title='Age')),
showlegend = FALSE)
regression.scatterplots[[3]] <- plot_ly(scaled.nums,
x = ~CompPrice,
name = 'CompPrice vs Sales Regression Line') %>%
add_markers(y = ~Sales,
name = 'CompPrice vs Sales Observations') %>%
add_lines(x = ~CompPrice,
y = fitted(fit.CompPrice)) %>%
layout(title = "CompPrice vs Sales",
yaxis=list(title='Sales',
xaxis=list(title='CompPrice')),
showlegend = FALSE)
regression.scatterplots[[4]] <- plot_ly(scaled.nums,
x = ~Price,
name = 'Price vs Sales Regression Line') %>%
add_markers(y = ~Sales,
name = 'Price vs Sales Observations') %>%
add_lines(x = ~Price,
y = fitted(fit.Price)) %>%
layout(title = "Price vs Sales",
yaxis=list(title='Sales',
xaxis=list(title='Price')),
showlegend = FALSE)
regression.scatterplotsLet’s compare the slopes:
scaled.nums %>%
plot_ly(y = ~Sales) %>%
add_lines(x= ~Population, y = fitted(fit.Pop),
name = "fit.Pop slope", line = list(shape = "linear")) %>%
add_lines(x= ~Age, y = fitted(fit.Age),
name = "fit.Age slope", line = list(shape = "linear")) %>%
add_lines(x= ~CompPrice, y = fitted(fit.CompPrice),
name = "fit.CompPrice slope", line = list(shape = "linear")) %>%
add_lines(x= ~Price, y = fitted(fit.Price),
name = "fit.Price slope", line = list(shape = "linear")) %>%
layout(title = "Regression Lines vs Sales (scaled)",
autosize=FALSE,
width=900,
yaxis=list(title='Sales'),
xaxis=list(title='Scaled Numeric Variable'))## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()Here’s a pretty graphic that doesn’t help me understand anything about the data.
y = scaled.nums$Sales
x = scaled.nums$Price
s <- subplot(
plot_ly(x = x, color = I("black"), type = 'histogram'),
plotly_empty(),
plot_ly(x = x, y = y, type = 'histogram2dcontour', showscale = F),
plot_ly(y = y, color = I("black"), type = 'histogram'),
nrows = 2, heights = c(0.2, 0.8), widths = c(0.8, 0.2),
shareX = TRUE, shareY = TRUE, titleX = FALSE, titleY = FALSE)## Warning: No trace type specified and no positional attributes specifiedlayout(s, showlegend = FALSE, autosize=FALSE,
width=700,
height=500,
yaxis=list(title='Sales'),
xaxis=list(title='Price'))## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()Categorical Plotting
First, let’s create a data frame that we can use:
categorical.by.sales = cbind(Sales = scaled.nums$Sales, cats)
str(categorical.by.sales)## 'data.frame': 400 obs. of 4 variables:
## $ Sales : num 0.7095 1.3185 0.9078 -0.0341 -1.1849 ...
## $ ShelveLoc: Factor w/ 3 levels "Bad","Good","Medium": 1 2 3 3 1 1 3 2 3 3 ...
## $ Urban : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 2 2 1 1 ...
## $ US : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 2 1 2 1 2 ...Here we can see all categorical by Sales. We suspected that ‘ShelveLoc’ would be important based on one of the early scatter plots. It seems that this is the case.
categorical.boxplots <- htmltools::tagList()
count = 1
for (i in names(categorical.by.sales[,-1])) {
categorical.boxplots[[count]] <- plot_ly(categorical.by.sales, x=categorical.by.sales[,i], y=categorical.by.sales$Sales,
type = "box",
boxpoints = "all",
jitter = .2,
pointpos = 0,
color =categorical.by.sales[,i],
colors='Set1',
name=paste(i)) %>%
layout(title = paste(i, "vs Sales (scaled)"),
showlegend = TRUE,
yaxis=list(title='Sales Standard Deviation'),
xaxis=list(title=i))
count = count + 1
}
categorical.boxplotsHere’s the same thing, but more musically:
categorical.violins <- htmltools::tagList()
count = 1
for (i in names(categorical.by.sales[,-1])) {
categorical.violins[[count]] <- plot_ly(categorical.by.sales, x=categorical.by.sales[,i], y=categorical.by.sales$Sales,
split = categorical.by.sales[,i],
type = 'violin',
colors='Set1',
name=paste(i),
box = list(visible = TRUE),
meanline = list(visible = TRUE)) %>%
layout(xaxis = list(title = "US"),
yaxis = list(title = "Sales",zeroline = FALSE))
count = count + 1
}
categorical.violinsLinear Regression
First, let’s merge the data set into a single data frame
scaled.merged <- cbind(categorical.by.sales[,-1], scaled.nums)
str(scaled.merged)## 'data.frame': 400 obs. of 11 variables:
## $ ShelveLoc : Factor w/ 3 levels "Bad","Good","Medium": 1 2 3 3 1 1 3 2 3 3 ...
## $ Urban : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 2 2 1 1 ...
## $ US : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 2 1 2 1 2 ...
## $ Sales : num 0.7095 1.3185 0.9078 -0.0341 -1.1849 ...
## $ CompPrice : num 0.849 -0.911 -0.781 -0.52 1.045 ...
## $ Income : num 0.155 -0.738 -1.203 1.12 -0.166 ...
## $ Advertising: num 0.656 1.408 0.506 -0.396 -0.547 ...
## $ Population : num 0.0757 -0.0328 0.0282 1.3649 0.51 ...
## $ Price : num 0.178 -1.385 -1.512 -0.794 0.515 ...
## $ Age : num -0.699 0.721 0.35 0.104 -0.946 ...
## $ Education : num 1.183 -1.4882 -0.725 0.0382 -0.3434 ...head(nums, 2)## Sales CompPrice Income Advertising Population Price Age Education
## 1 9.50 138 73 11 276 120 42 17
## 2 11.22 111 48 16 260 83 65 10tail(nums, 2)## Sales CompPrice Income Advertising Population Price Age Education
## 399 5.94 100 79 7 284 95 50 12
## 400 9.71 134 37 0 27 120 49 16head(scaled.merged, 2)## ShelveLoc Urban US Sales CompPrice Income Advertising
## 1 Bad Yes Yes 0.7094877 0.8493913 0.1551667 0.656355
## 2 Good Yes Yes 1.3185281 -0.9113430 -0.7381360 1.408194
## Population Price Age Education
## 1 0.07572445 0.177601 -0.6989069 1.182968
## 2 -0.03284107 -1.385119 0.7208201 -1.488250tail(scaled.merged, 2)## ShelveLoc Urban US Sales CompPrice Income Advertising
## 399 Bad Yes Yes -0.5510841 -1.6286792 0.3695593 0.05488421
## 400 Good Yes Yes 0.7838473 0.5885417 -1.1311891 -0.99768973
## Population Price Age Education
## 399 0.1300072 -0.8782909 -0.2050888 -0.7250447
## 400 -1.6138265 0.1776010 -0.2668161 0.8013652First, let’s look at some things that may give us trouble. Luckily it looks like the only serious correlation is with our dependent variable. We’ll want to watch the ‘Price’ vs ‘CompPrice’ relationship.
res <- cor(scaled.nums)
corrplot(res, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
It appears that residuals are roughly symmetrical around 0. That’s strange. Mostly due to a relatively poor overall fit. Note how close to zero most of the coefficient estimates are.
simple.lm <- lm(Sales~., data=scaled.merged)
simple.summary <- summary(simple.lm)
print(simple.summary)##
## Call:
## lm(formula = Sales ~ ., data = scaled.merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.01598 -0.24463 0.00748 0.23496 1.20797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.73292 0.05999 -12.217 < 2e-16 ***
## ShelveLocGood 1.71742 0.05422 31.678 < 2e-16 ***
## ShelveLocMedium 0.69286 0.04465 15.516 < 2e-16 ***
## UrbanYes 0.04351 0.04000 1.088 0.277
## USYes -0.06519 0.05306 -1.229 0.220
## CompPrice 0.50397 0.02252 22.378 < 2e-16 ***
## Income 0.15660 0.01828 8.565 2.58e-16 ***
## Advertising 0.28987 0.02619 11.066 < 2e-16 ***
## Population 0.01085 0.01933 0.561 0.575
## Price -0.79946 0.02239 -35.700 < 2e-16 ***
## Age -0.26413 0.01825 -14.472 < 2e-16 ***
## Education -0.01958 0.01830 -1.070 0.285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3608 on 388 degrees of freedom
## Multiple R-squared: 0.8734, Adjusted R-squared: 0.8698
## F-statistic: 243.4 on 11 and 388 DF, p-value: < 2.2e-16Linear Models and Subsets
Let’s do the same thing, but control the subsets using leaps
regfit.full=regsubsets(Sales~., data=scaled.merged, nvmax=5)
reg.summary=summary(regfit.full)
print(reg.summary)## Subset selection object
## Call: (function(input_file, encoding) {
## out_dir <- "docs"
## rmarkdown::render(input_file, encoding = encoding, output_file = file.path(dirname(input_file),
## out_dir, "carseats.html"))
## })("/Users/milk/Desktop/git/EDA_r_ece579/carseats.Rmd", encoding = "UTF-8")
## 11 Variables (and intercept)
## Forced in Forced out
## ShelveLocGood FALSE FALSE
## ShelveLocMedium FALSE FALSE
## UrbanYes FALSE FALSE
## USYes FALSE FALSE
## CompPrice FALSE FALSE
## Income FALSE FALSE
## Advertising FALSE FALSE
## Population FALSE FALSE
## Price FALSE FALSE
## Age FALSE FALSE
## Education FALSE FALSE
## 1 subsets of each size up to 5
## Selection Algorithm: exhaustive
## ShelveLocGood ShelveLocMedium UrbanYes USYes CompPrice Income
## 1 ( 1 ) "*" " " " " " " " " " "
## 2 ( 1 ) "*" " " " " " " " " " "
## 3 ( 1 ) "*" " " " " " " "*" " "
## 4 ( 1 ) "*" " " " " " " "*" " "
## 5 ( 1 ) "*" "*" " " " " "*" " "
## Advertising Population Price Age Education
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) " " " " "*" " " " "
## 3 ( 1 ) " " " " "*" " " " "
## 4 ( 1 ) "*" " " "*" " " " "
## 5 ( 1 ) "*" " " "*" " " " "We’ll just take code straight from the example on Canvas…
par(mfrow=c(2,2))
plot(reg.summary$rss,xlab="Number of Variables",ylab="RSS",type="l")
plot(reg.summary$adjr2,xlab="Number of Variables",ylab="Adjusted RSq",type="l")
plot(reg.summary$cp,xlab="Number of Variables",ylab="Cp",type='l')
plot(reg.summary$bic,xlab="Number of Variables",ylab="BIC",type='l')
plot(regfit.full,scale="r2")
plot(regfit.full,scale="adjr2")
plot(regfit.full,scale="Cp")
plot(regfit.full,scale="bic")
Interaction Terms
Here we define a new model with some interaction terms: a. Income and Advertising b. Income and CompPrice c. Price and Age
interaction.lm <- lm(Sales~. + Income*Advertising + Income*CompPrice + Price*Age, data=scaled.merged)
interaction.summary <- summary(interaction.lm)
print(interaction.summary)##
## Call:
## lm(formula = Sales ~ . + Income * Advertising + Income * CompPrice +
## Price * Age, data = scaled.merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.04967 -0.23955 -0.00936 0.24591 1.19774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.730051 0.059236 -12.324 < 2e-16 ***
## ShelveLocGood 1.714324 0.053519 32.032 < 2e-16 ***
## ShelveLocMedium 0.680563 0.044177 15.405 < 2e-16 ***
## UrbanYes 0.045257 0.039380 1.149 0.25117
## USYes -0.069622 0.052329 -1.330 0.18415
## CompPrice 0.507567 0.022131 22.935 < 2e-16 ***
## Income 0.152164 0.018025 8.442 6.43e-16 ***
## Advertising 0.289455 0.025756 11.238 < 2e-16 ***
## Population 0.007224 0.018985 0.381 0.70377
## Price -0.797242 0.022003 -36.233 < 2e-16 ***
## Age -0.260783 0.017978 -14.505 < 2e-16 ***
## Education -0.024248 0.018063 -1.342 0.18027
## Income:Advertising 0.046378 0.018170 2.552 0.01108 *
## CompPrice:Income -0.059184 0.018911 -3.130 0.00188 **
## Price:Age 0.013171 0.017912 0.735 0.46259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3538 on 385 degrees of freedom
## Multiple R-squared: 0.8792, Adjusted R-squared: 0.8748
## F-statistic: 200.1 on 14 and 385 DF, p-value: < 2.2e-16interaction.lm.subsets <- regsubsets(Sales~. + Income*Advertising + Income*CompPrice + Price*Age,
data=scaled.merged, nvmax=5)
interaction.subsets.summary <- summary(interaction.lm.subsets)
print(interaction.subsets.summary)## Subset selection object
## Call: (function(input_file, encoding) {
## out_dir <- "docs"
## rmarkdown::render(input_file, encoding = encoding, output_file = file.path(dirname(input_file),
## out_dir, "carseats.html"))
## })("/Users/milk/Desktop/git/EDA_r_ece579/carseats.Rmd", encoding = "UTF-8")
## 14 Variables (and intercept)
## Forced in Forced out
## ShelveLocGood FALSE FALSE
## ShelveLocMedium FALSE FALSE
## UrbanYes FALSE FALSE
## USYes FALSE FALSE
## CompPrice FALSE FALSE
## Income FALSE FALSE
## Advertising FALSE FALSE
## Population FALSE FALSE
## Price FALSE FALSE
## Age FALSE FALSE
## Education FALSE FALSE
## Income:Advertising FALSE FALSE
## CompPrice:Income FALSE FALSE
## Price:Age FALSE FALSE
## 1 subsets of each size up to 5
## Selection Algorithm: exhaustive
## ShelveLocGood ShelveLocMedium UrbanYes USYes CompPrice Income
## 1 ( 1 ) "*" " " " " " " " " " "
## 2 ( 1 ) "*" " " " " " " " " " "
## 3 ( 1 ) "*" " " " " " " "*" " "
## 4 ( 1 ) "*" " " " " " " "*" " "
## 5 ( 1 ) "*" "*" " " " " "*" " "
## Advertising Population Price Age Education Income:Advertising
## 1 ( 1 ) " " " " " " " " " " " "
## 2 ( 1 ) " " " " "*" " " " " " "
## 3 ( 1 ) " " " " "*" " " " " " "
## 4 ( 1 ) "*" " " "*" " " " " " "
## 5 ( 1 ) "*" " " "*" " " " " " "
## CompPrice:Income Price:Age
## 1 ( 1 ) " " " "
## 2 ( 1 ) " " " "
## 3 ( 1 ) " " " "
## 4 ( 1 ) " " " "
## 5 ( 1 ) " " " "Variable Significance
Below we print the coefficients for the 5th model using the default model selection criteria. All coefficients are relatively small, as we would expect from the EDA above. This pretty much confirms what I would have guessed by looking at the data against sales. We still want to watch out for confounding between ‘Price’ and ‘CompPrice.’
coef(interaction.lm.subsets, 5)## (Intercept) ShelveLocGood ShelveLocMedium CompPrice
## -0.6995348 1.6746198 0.6277225 0.5090177
## Advertising Price
## 0.2832405 -0.7846476Second Interaction Model
First, drop columns unneeded from analysis:
scaled.merged.slim <- scaled.merged[ , -which(names(scaled.merged) %in% c("US","Urban"))]A few hyper parameters we’d like to be consistent for all models
nvmax <- 3Forward Selection:
interaction.subset.fwd <- regsubsets(Sales~. + Income*Advertising + Income*CompPrice + Income*Age,
data=scaled.merged.slim,
nvmax = nvmax,
method="forward")
fwd.subset.summary <- summary(interaction.subset.fwd)
coef(interaction.subset.fwd, 1:nvmax)## [[1]]
## (Intercept) ShelveLocGood
## -0.259671 1.221981
##
## [[2]]
## (Intercept) ShelveLocGood Price
## -0.2708256 1.2744733 -0.4688868
##
## [[3]]
## (Intercept) ShelveLocGood CompPrice Price
## -0.2709362 1.2749938 0.4932455 -0.7573701Backward Selection:
This is really strange. I can’t seem to find any documentation about this, but it appears that this model is actually ‘forward.’
interaction.subset.bk <- regsubsets(Sales~. + Income*Advertising + Income*CompPrice + Income*Age,
data=scaled.merged.slim,
nvmax = nvmax,
method="backward")
bk.subset.summary <- summary(interaction.subset.bk)
coef(interaction.subset.bk, 1:nvmax)## [[1]]
## (Intercept) ShelveLocGood
## -0.259671 1.221981
##
## [[2]]
## (Intercept) ShelveLocGood Price
## -0.2708256 1.2744733 -0.4688868
##
## [[3]]
## (Intercept) ShelveLocGood CompPrice Price
## -0.2709362 1.2749938 0.4932455 -0.7573701Exhaustive
interaction.subset.ex <- regsubsets(Sales~. + Income*Advertising + Income*CompPrice + Income*Age,
data=scaled.merged.slim,
nvmax = nvmax,
method="exhaustive")
ex.subset.summary <- summary(interaction.subset.ex)
coef(interaction.subset.ex, 1:nvmax)## [[1]]
## (Intercept) ShelveLocGood
## -0.259671 1.221981
##
## [[2]]
## (Intercept) ShelveLocGood Price
## -0.2708256 1.2744733 -0.4688868
##
## [[3]]
## (Intercept) ShelveLocGood CompPrice Price
## -0.2709362 1.2749938 0.4932455 -0.7573701This is a list that may come in handy.
model.list = list(list("Forward", interaction.subset.fwd, fwd.subset.summary),
list("Backward", interaction.subset.bk, bk.subset.summary),
list("Exhaustive", interaction.subset.ex, ex.subset.summary))Evaluation Metric Plotting
It is interesting to see that each model selected the same variables, in the same order.
par(mfrow=c(2,2))
plot(fwd.subset.summary$rss,xlab="Number of Variables (forward)",ylab="RSS",type="l")
plot(fwd.subset.summary$adjr2,xlab="Number of Variables (forward)",ylab="Adjusted RSq",type="l")
plot(fwd.subset.summary$cp,xlab="Number of Variables (forward)",ylab="Cp",type='l')
plot(fwd.subset.summary$bic,xlab="Number of Variables (forward)",ylab="BIC",type='l')
par(mfrow=c(2,2))
plot(bk.subset.summary$rss,xlab="Number of Variables (backward)",ylab="RSS",type="l")
plot(bk.subset.summary$adjr2,xlab="Number of Variables (backward)",ylab="Adjusted RSq",type="l")
plot(bk.subset.summary$cp,xlab="Number of Variables (backward)",ylab="Cp",type='l')
plot(bk.subset.summary$bic,xlab="Number of Variables (backward)",ylab="BIC",type='l')
par(mfrow=c(2,2))
plot(ex.subset.summary$rss,xlab="Number of Variables (exhaustive)",ylab="RSS",type="l")
plot(ex.subset.summary$adjr2,xlab="Number of Variables (exhaustive)",ylab="Adjusted RSq",type="l")
plot(ex.subset.summary$cp,xlab="Number of Variables (exhaustive)",ylab="Cp",type='l')
plot(ex.subset.summary$bic,xlab="Number of Variables (exhaustive)",ylab="BIC",type='l')
Model Equations
Here we print the final equations for each model. Not, they are all the same.
for (mod.obj in model.list) {
mod.name <- mod.obj[[1]]
best.bic <- min(mod.obj[[3]]$bic)
mod.num <- which.min(mod.obj[[3]]$bic)
mod.cc <- coef(mod.obj[[2]], mod.num)
mod.equation.format <- paste("Y =", paste(round(mod.cc[1],2),
paste(round(mod.cc[-1],2),
names(mod.cc[-1]),
sep=" * ", collapse=" + "),
sep=" + "), "+ e")
print(paste("Model Selection Method: ",mod.name))
print(paste("Min BIC:", best.bic))
print(paste("Model Number: ", mod.num))
print(paste("Model Equation: ", mod.equation.format))
print("")
}## [1] "Model Selection Method: Forward"
## [1] "Min BIC: -373.710213587368"
## [1] "Model Number: 3"
## [1] "Model Equation: Y = -0.27 + 1.27 * ShelveLocGood + 0.49 * CompPrice + -0.76 * Price + e"
## [1] ""
## [1] "Model Selection Method: Backward"
## [1] "Min BIC: -373.710213587368"
## [1] "Model Number: 3"
## [1] "Model Equation: Y = -0.27 + 1.27 * ShelveLocGood + 0.49 * CompPrice + -0.76 * Price + e"
## [1] ""
## [1] "Model Selection Method: Exhaustive"
## [1] "Min BIC: -373.710213587368"
## [1] "Model Number: 3"
## [1] "Model Equation: Y = -0.27 + 1.27 * ShelveLocGood + 0.49 * CompPrice + -0.76 * Price + e"
## [1] ""Formally, we are looking for: \[\hat{Y} = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_3 + \epsilon\] Our model yields: \[\hat{Y} = -0.27 + 1.27(ShelveLocGood) + 0.49(CompPrice) + -0.76(Price)\]
Appendix A: PCA
This is what I would try (though, I think there is enough evidence to suggest that this data is synthetic.) PCA doesn’t help us very much, but we’ll see…
scaled.nums <- dplyr::select_if(scaled.merged, is.numeric)
scaled.nums <- scaled.nums[,-1]pc_result = principal(scaled.nums, nfactors = 3)
print(pc_result$Vaccounted)## RC1 RC2 RC3
## SS loadings 1.6233084 1.2805798 1.0666582
## Proportion Var 0.2319012 0.1829400 0.1523797
## Cumulative Var 0.2319012 0.4148412 0.5672209
## Proportion Explained 0.4088375 0.3225198 0.2686427
## Cumulative Proportion 0.4088375 0.7313573 1.0000000print(pc_result$weights)## RC1 RC2 RC3
## CompPrice 0.53157541 -0.06083110 0.030590603
## Income -0.01204453 -0.14471355 -0.751358873
## Advertising 0.03963226 0.59649172 0.009275437
## Population -0.02336339 0.63042187 0.026481975
## Price 0.53696973 0.04180948 0.010454833
## Age -0.20779551 -0.03119157 0.245615463
## Education -0.04934910 -0.08172391 0.567451434fa.diagram(pc_result)
biplot(pc_result)
pca.df <- cbind(pc_result$scores, scaled.merged[1:4])
pca.df.h1 <- one_hot(as.data.table(pca.df))
head(pca.df.h1)## RC1 RC2 RC3 ShelveLoc_Bad ShelveLoc_Good
## 1: 0.6561080 0.29767325 0.4189620 1 0
## 2: -1.2390863 1.02275930 -0.1430276 0 1
## 3: -1.2300855 0.52627007 0.5440203 0 0
## 4: -0.7871945 0.45417596 -0.7861223 0 0
## 5: 1.0142239 0.03510677 -0.2563580 1 0
## 6: -1.4017045 1.16533839 -0.3315862 1 0
## ShelveLoc_Medium Urban_No Urban_Yes US_No US_Yes Sales
## 1: 0 0 1 0 1 0.70948774
## 2: 0 0 1 0 1 1.31852808
## 3: 1 0 1 0 1 0.90777994
## 4: 1 0 1 0 1 -0.03410803
## 5: 0 0 1 1 0 -1.18491100
## 6: 0 1 0 0 1 1.17334986Empirically I tried a GAM model and a SVM. These weren’t impressive enough out of the box to show here. On to the next task…